Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Epipolar-Free 3D Gaussian Splatting for Generalizable Novel View Synthesis
Authors: Zhiyuan Min, Yawei Luo, Jianwen Sun, Yi Yang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate e Free Splat on wide-baseline novel view synthesis tasks using the Real Estate10K and ACID datasets. Extensive experiments demonstrate that e Free Splat surpasses state-of-the-art baselines that rely on epipolar priors, achieving superior geometry reconstruction and novel view synthesis quality. |
| Researcher Affiliation | Academia | Zhiyuan Min1 Yawei Luo1, Jianwen Sun2 Yi Yang1 1Zhejiang University 2Central China Normal University |
| Pseudocode | No | The paper does not contain a clearly labeled section or figure titled 'Pseudocode' or 'Algorithm'. |
| Open Source Code | Yes | Project page: https://tatakai1.github.io/efreesplat/. |
| Open Datasets | Yes | e Free Splat is trained on Real Estate10K [72] and ACID [26]. |
| Dataset Splits | No | Following pixel Splat [6], we use the provided training and testing splits and evaluate three novel view images on each test scene. The paper does not explicitly mention a 'validation' split or its specific proportions/counts. |
| Hardware Specification | Yes | All models are trained on 4 RTX-4090 GPUs for 300, 000 iterations using the Adam optimizer [24]. |
| Software Dependencies | No | The paper mentions using specific models like 'Vi T-B vision transformer' and 'Cro Co v2' and an 'Adam optimizer', but it does not specify version numbers for any software libraries or dependencies (e.g., PyTorch, TensorFlow, specific Python versions). |
| Experiment Setup | Yes | All models are trained on 4 RTX-4090 GPUs for 300, 000 iterations using the Adam optimizer [24]. The per-GPU batch size during training is 4. ... using the Adam optimizer with a learning rate 2e-4. ... the resolution of our training and testing images for fair comparison (256x256). |